Senior IT PMO Specialist

McDermott•Houston, TX

1d•Hybrid

About The Position

The IT PMO Senior AI Project Manager requires 5 to 10 years of PMP PM experience delivering complex initiatives in a large, global organization. The role partners with executive sponsors, business functions, IT, data, cybersecurity, legal, privacy, compliance, procurement, and vendors to deliver secure, scalable solutions across HR, Finance, Supply Chain, Engineering, Fabrication, Construction, Sales, Engineering, Construction, QHSES, and Marine/Vessel operations.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Information Systems, MIS, Engineering, Business, or a related field; advanced degree preferred.
Current Project Management Professional (PMP) certification required; Agile, Scrum, SAFe, PRINCE2, change management, cloud, data, cybersecurity, or AI certification preferred.
5 to 10 years of progressively responsible IT PMP project management experience in a large, matrixed, global organization.
Proven delivery of complex, business-facing projects within approved scope, schedule, and budget, with strong quality, adoption, stakeholder satisfaction, and measurable outcomes.
Expertise across PMI project management domains, predictive and adaptive delivery, financial management, governance, risk, procurement, vendor management, and executive reporting.
Strong leadership, business acumen, facilitation, negotiation, conflict resolution, organization, communication, and influence without authority.
Experience across infrastructure, cloud, cybersecurity, data and analytics, enterprise applications, integrations, workplace technology, software delivery, or IT service management.
Experience supporting functions such as HR, Finance, Supply Chain, Manufacturing, Sales, Engineering, Construction, QHSES, or Marine/Vessel operations; global energy, EPCI, industrial, or maritime experience preferred.
Thorough understanding of SDLC, architecture governance, requirements, testing, release management, operational readiness, data governance, privacy, cybersecurity, compliance, and audit readiness.
Experience managing AI, Generative AI, Agentic AI, machine learning, intelligent automation, data science, analytics, or other emerging-technology initiatives is strongly preferred.
Practical understanding of LLMs, prompts, retrieval-augmented generation, agents, APIs, cloud AI services, data pipelines, AI evaluation, Responsible AI, lifecycle monitoring, licensing, and consumption costs.
Ability to challenge unsupported claims, distinguish demonstrations from production-ready capabilities, and require evidence of performance, controls, adoption, scalability, and business value.
Ability to operate independently, manage competing priorities and ambiguity, lead globally distributed teams, and make timely, risk-informed recommendations.

Nice To Haves

advanced degree preferred
Agile, Scrum, SAFe, PRINCE2, change management, cloud, data, cybersecurity, or AI certification preferred

Responsibilities

Lead projects from initiation through planning, execution, monitoring and controlling, operational transition, benefits realization, and closure.
Apply PMI/PMP practices using Waterfall, Agile, or Hybrid delivery methods appropriate to project complexity, risk, and business readiness.
Define the charter, scope, requirements, work breakdown structure, schedule, budget, resources, quality, procurement, communications, stakeholder engagement, and benefits plans.
Maintain integrated risk, issue, dependency, assumption, decision, and change logs; establish owners, response plans, escalation paths, and timely closure.
Lead global, cross-functional teams and coordinate business owners, IT, vendors, managed service providers, and subject-matter experts without relying on direct authority.
Establish governance, RACI assignments, steering committees, tollgates, decision rights, and concise executive reporting covering progress, financials, milestones, risks, issues, changes, decisions, and value.
Manage scope, schedule, cost, quality, resources, contracts, statements of work, vendor deliverables, and forecasts; analyze variance and implement recovery actions.
Oversee requirements, design, build or configuration, integration, data migration, testing, user acceptance, deployment, cutover, hypercare, operational readiness, knowledge transfer, and support transition.
Enforce quality assurance, requirements traceability, acceptance criteria, SDLC compliance, formal change control, and completion of required approvals before release.
Lead stakeholder engagement, communications, training, business change, adoption measurement, and resistance management to embed sustainable ways of working.
Define and track benefits such as cost savings, productivity, cycle-time reduction, risk reduction, revenue enablement, service improvement, and user adoption; transition ownership to the business.
Present technical complexity, delivery choices, risks, investment needs, and recommendations in clear business language to executive and non-technical audiences.
For AI initiatives, define the business use case, value hypothesis, target users, process impact, autonomy level, human oversight, acceptable-use boundaries, and measurable success crit
Integrate AI governance and Responsible AI reviews into project plans and tollgates, including data, architecture, cybersecurity, privacy, legal, compliance, records, and third-party risk requirements.
Manage AI risks involving sensitive data, intellectual property, bias, fairness, transparency, explainability, hallucinations, prompt or model manipulation, security, vendor models, autonomous actions, and regulatory obligations.
Coordinate approved data sources, data quality and lineage, model and vendor details, grounding approach, prompts and configurations, evaluation datasets, performance thresholds, traceability, and documented limitations.
Plan AI testing for accuracy, relevance, reliability, safety, security, privacy, bias, robustness, adversarial scenarios, fallback behavior, reproducibility, and user acceptance.
Ensure human oversight, approvals, segregation of duties, auditability, exception handling, rollback or shutdown procedures, accountable service ownership, monitoring, drift detection, incident response, revalidation, and retirement criteria.
Manage AI pilots with time-boxed learning objectives, secure environments, objective go/no-go criteria, production ownership, scaling decisions, and verified value.
Drive PMO continuous improvement through lessons learned, reusable standards, delivery metrics, coaching, and governance enhancements.